Hapori: context-based local search for mobile phones using community behavioral modeling and similarity

ABSTRACT

Local search engines are very popular but limited. We present Hapori, a next-generation local search technology for mobile phones that not only takes into account location in the search query but richer context such as the time, weather and the activity of the user. Hapori also builds behavioral models of users and exploits the similarity between users to tailor search results to personal tastes rather than provide static geo-driven points of interest. We discuss the design, implementation and evaluation of the Hapori framework which combines data mining, information preserving embedding and distance metric learning to address the challenge of creating efficient multidimensional models from context-rich local search logs. Our experimental results using 80,000 queries extracted from search logs show that contextual and behavioral similarity information can improve the relevance of local search results by up to ten times when compared to the results currently provided by commercially available search engine technology.

References

1. }}G. D. Abowd, et al. Cyberguide: a mobile context-aware tour guide. Wirel. Netw., 3(5):421--433, 1997.
2. }}C. M. Bishop. Pattern Recognition and Machine Learning (Information Science and Statistics). Springer-Verlag New York, Inc., Secaucus, NJ, 2006.
3. }}A. T. Campbell, et al. The rise of people-centric sensing. IEEE Internet Computing, 12:12--21, 2008.
4. }}K. M. Carter, et al. Fine: Fisher information nonparametric embedding. IEEE Trans. Pattern Anal. Mach. Intell., 31(11):2093--2098, 2009.
5. }}K. Cheverst, et al. Developing a context-aware electronic tourist guide: some issues and experiences. In CHI '00: Proceedings of the SIGCHI conference on Human factors in computing systems, pages 17--24, New York, NY, 2000. ACM.
6. }}K. Church and B. Smyth. Who, what, where & when: a new approach to mobile search. In IUI '08: Proceedings of the 13th international conference on Intelligent user interfaces, pages 309--312, New York, NY, 2008. ACM.
7. }}K. Church, et al. Mobile information access: A study of emerging search behavior on the mobile internet. ACM Trans. Web, 1(1):4, 2007.
8. }}B. Croft, D. Metzler, and T. Strohman. Search Engines: Information Retrieval in Practice. Addison-Wesley Publishing Company, USA, 2009.
9. }}J. Froehlich, et al. Voting with your feet: An investigative study of the relationship between place visit behavior and preference. In Ubicomp, volume 4206 of Lecture Notes in Computer Science, pages 333--350. Springer, 2006.
10. }}S. Hattori, T. Tezuka, and K. Tanaka. Context-aware query refinement for mobile web search. In SAINT-W '07: Proceedings of the 2007 International Symposium on Applications and the Internet Workshops, page 15, Washington, DC, 2007. IEEE Computer Society.
11. }}Microsoft. Mobile Bing Local. http://m.bing.com/.
12. }}B. N. Miller, et al. Movielens unplugged: experiences with an occasionally connected recommender system. In IUI '03: Proceedings of the 8th international conference on Intelligent user interfaces, pages 263--266, New York, NY, 2003.
13. }}P. Resnick and H. R. Varian. Recommender systems. Commun. ACM, 40(3):56--58, 1997.
14. }}B. Smyth, et al. Exploiting query repetition and regularity in an adaptive community-based web search engine. User Modeling and User-Adapted Interaction, 14(5):383--423, 2005.
15. }}J. Teevan, S. T. Dumais, and E. Horvitz. Personalizing search via automated analysis of interests and activities. In SIGIR '05: Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval, pages 449--456, New York, NY, 2005. ACM.
16. }}K. Q. Weinberger and L. K. Saul. Distance metric learning for large margin nearest neighbor classification. J. Mach. Learn. Res., 10:207--244, 2009.
17. }}J. Yi, F. Maghoul, and J. Pedersen. Deciphering mobile search patterns: a study of yahoo! mobile search queries. In WWW '08: Proceeding of the 17th international conference on World Wide Web, pages 257--266, New York, NY, 2008. ACM.

Index Terms

• Information systems

  • Information systems applications